Course detail

# Applied Analytical Statistics

FP-BAASEAcad. year: 2022/2023

Students will gain knowledge of random variable, mathematical statistics, categorical analysis, methods of regression analysis and analysis of time series describing economics and social events.

Language of instruction

Number of ECTS credits

Mode of study

Guarantor

Department

Offered to foreign students

Learning outcomes of the course unit

Prerequisites

Co-requisites

Planned learning activities and teaching methods

Exercise promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The course-unit credit is awarded on the following conditions (max. 40 points):

- elaboration of semestral assignments.

The exam (max. 60 points)

- has a written form.

In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:

- points achieved in semestral assignments,

- points achieved by solving examples,

- points achieved by answering theoretical questions.

The grades and corresponding points:

A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):

- elaboration of semestral assignments.

The exam (max. 60 points)

- has a written form.

In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:

- points achieved in semestral assignments,

- points achieved by solving examples,

- points achieved by answering theoretical questions.

The grades and corresponding points:

A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

Course curriculum

2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)

3. Bivariate random variables (correlation)

4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)

5. Data sample analysis

6. Parameters’ estimation (point and interval estimates)

7. Test of statistical hypothesis (basic concepts and procedure)

8. Basic parametric tests (t-test, F-test, ANOVA)

9. Index analysis

10. Individual and composite indexes

11. Linear regression model (basic concepts, the least square method)

12. Non-linear regression model (linearizable and non-linearizable regression models)

13. Time series analysis (basic characteristics, decomposition)

Work placements

Aims

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is not mandatory but is recommended. Attendance at seminars is controlled.

Recommended optional programme components

Prerequisites and corequisites

Basic literature

MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5. (EN)

Recommended reading

BOX, George E. P., William Gordon HUNTER a J. Stuart HUNTER, 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. B.m.: Wiley. ISBN 978-0-471-09315-2. (EN)

MONTGOMERY, Douglas C., 2008. Design and Analysis of Experiments. B.m.: John Wiley & Sons. ISBN 978-0-470-12866-4. (EN)

eLearning

**eLearning:**currently opened course

#### Type of course unit

Lecture

Teacher / Lecturer

Syllabus

2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)

3. Bivariate random variables (correlation)

4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)

5. Data sample analysis

6. Parameters’ estimation (point and interval estimates)

7. Test of statistical hypothesis (basic concepts and procedure)

8. Basic parametric tests (t-test, F-test, ANOVA)

9. Index analysis

10. Individual and composite indexes

11. Linear regression model (basic concepts, the least square method)

12. Non-linear regression model (linearizable and non-linearizable regression models)

13. Time series analysis (basic characteristics, decomposition)

Exercise

Teacher / Lecturer

Syllabus

The content of the exercises corresponds to the content of the lectures.

eLearning

**eLearning:**currently opened course